Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R² value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R²: ~0.60.

中文翻译：

---

半干旱灌溉景观上使用协调Landsat和Sentinel-2表面反射数据评估叶面积指数模型

叶面积指数（LAI）是作物生长发育的重要指标。对于许多农业应用而言，基于农场的基于卫星的LAI估算通常需要中至高空间分辨率的近日图像。来自不同的正在进行的卫星任务Sentinel 2（ESA）和Landsat 8（NASA）的数据的结合提供了这个机会。在这项研究中，我们评估了三种方法产生的叶面积指数，即适用于NASA产生的Harmonized Landsat-8和Sentinel-2（HLS）表面反射率的现有植被指数（VI）关系，SNAP生物物理模型以及Sentinel-2的THEIA L2A表面反射产品。比较是在2018年和2019年生长季节，使用大量现场LAI和其他生物物理测量结果收集的，在贝卡（黎巴嫩）的农业计划中进行的。研究的主要农作物包括草药（例如，大麻：大麻，薄荷：薄荷，和其他人），土豆（马铃薯），蔬菜（如豆类：菜豆，甘蓝：甘蓝，胡萝卜：胡萝卜。亚种黄瓜， 和别的）。此外，还研究了作物特定高度和地上生物量与LAI的关系。结果表明，在测试的经验VI关系中，基于EVI2的HLS模型在统计上表现最佳，特别是最初为小麦（RMSE：1.27），玉米（RMSE：1.34）和大田作物（RMSE：1.38）开发的LAI模型）。通过欧洲航天局（ESA）前哨应用平台（SNAP）生物物理处理器得出的LAI低估了LAI，并且提供的准确度较低（RMSE为1.72）。此外，与HLS-EVI2模型（RMSE为1.38）相比，S2 SeLI LAI算法（来自SNAP生物物理处理器）产生了可接受的精度水平，但在高LAI值下被低估了。我们的研究结果表明，LAI-VI关系总体而言，特定于作物，具有线性和非线性回归形式。在所检查的指标中，将所有农作物组合在一起时，EVI2的表现优于其他植被指标，因此可以确定为最适合异质景观半干旱灌溉区农作物统一算法的指标。此外，我们的分析表明，观察到的高度-LAI关系是特定于作物的，并且与马铃薯的R ²值为0.82，小麦的R ²值为0.79，大麻和烟草的R ²值为0.50。线性回归从观测的高度和LAI估算马铃薯的新鲜和干燥地上生物量的能力是合理的，得出R ²：〜0.60。